在模仿中精进数据可视化_R语言绘制复杂的聚类热图
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在模仿中精进数据可视化该系列推文中，我们将从各大顶级学术期刊的Figure入手，
解读文章的绘图思路，
模仿文章的作图风格，
构建适宜的绘图数据，
并且将代码应用到自己的实际论文中。
绘图缘由：小伙伴们总会展示出一些非常好看且精美的图片。我大概率会去学习和复现一下。其实每个人的时间和精力都非常有限和异常宝贵的。之所以我会去做，主要有以下原因：
图片非常好看，我自己看着也手痒痒 图片我自己在Paper也用的上，储备着留着用 保持了持续学习的状态
论文
图片
复现
直接上代码：
加载R包
rm(list = ls())
####----load R Package----####
library(tidyverse)
library(readxl)
library(ggh4x)
library(ggtree)
library(tidytree)
library(treeio)
library(aplot)
library(ggfun)
source("R/tree.R")
加载数据
####----load Data----####
test_df <- read_xlsx(path = "Input/Example2.xlsx", col_names = T)
test_df1 <- test_df %>%
tidyr::pivot_longer(cols = -c(1:3), values_to = "Value", names_to = "Gene")
test_df2 <- test_df %>% dplyr::select(4:51)
row_clust <- hclust(dist(t(test_df2), method = "euclidean"), method = "complete")
roworder <- row_clust$labels[row_clust$order]
开始绘图
####----Plot----####
p <- ggplot(data = test_df1 %>%
dplyr::mutate(Gene = factor(Gene, levels = roworder, ordered = T))) +
geom_tile(aes(x = Tissue, y = Gene), color = "#969696", fill = NA) +
geom_point(aes(x = Tissue, y = Gene, color = Value, size = Value)) +
facet_nested(~Tissue+Sex+Timepoint,
scales = "free",
strip = strip_nested(
background_x = elem_list_rect(fill = c(
rep(c("#377eb8","#4daf4a"), times = 3),
rep(c("#9e9ac8", "#fe9929"), times = 6),
rep(c("#fde0dd", "#fcc5c0", "#fa9fb5", "#f768a1"),times = 12)
))
)) +
labs(x = "", y = "") +
scale_y_discrete(position = "right") +
scale_color_gradient2(low = "#4d9221", mid = "#f7f7f7", high = "#c51b7d", midpoint = 50) +
theme(
panel.background = element_rect(fill = NA, color = NA),
panel.spacing.x = unit(c(rep(c(0,0,0,0.1), times = 11), c(0,0,0)), "cm"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.background = element_roundrect(color = "#525252")
)
row_p <- create_tree(row_clust, n = 6, num = 8)
p_combine <- p %>%
insert_left(row_p, width = 0.2)
ggsave(filename = "Output/p.pdf",
plot = p_combine,
height = 15,
width = 20)
版本信息
####----sessionInfo----####
R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS 15.1.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Asia/Shanghai
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggfun_0.1.5 aplot_0.2.3 treeio_1.26.0 tidytree_0.4.5 ggtree_3.10.0
[6] ggh4x_0.2.8.9000 readxl_1.4.3 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[11] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[16] ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] yulab.utils_0.1.7 utf8_1.2.4 generics_0.1.3 ggplotify_0.1.2
[5] stringi_1.8.3 lattice_0.22-5 hms_1.1.3 digest_0.6.37
[9] magrittr_2.0.3 grid_4.3.0 timechange_0.2.0 jsonlite_1.8.9
[13] cellranger_1.1.0 writexl_1.4.2 ape_5.8 fansi_1.0.6
[17] scales_1.3.0 textshaping_0.3.7 lazyeval_0.2.2 cli_3.6.3
[21] rlang_1.1.4 munsell_0.5.1 withr_3.0.1 tools_4.3.0
[25] parallel_4.3.0 tzdb_0.4.0 colorspace_2.1-1 gridGraphics_0.5-1
[29] vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4 fs_1.6.5
[33] ragg_1.2.6 pkgconfig_2.0.3 pillar_1.9.0 gtable_0.3.5
[37] glue_1.8.0 Rcpp_1.0.13 systemfonts_1.1.0 tidyselect_1.2.1
[41] rstudioapi_0.15.0 farver_2.1.2 patchwork_1.2.0.9000 nlme_3.1-163
[45] labeling_0.4.3 compiler_4.3.0
历史绘图合集
公众号推文一览
进化树合集
环状图
散点图
基因家族合集
换一个排布方式：
首先查看基础版热图:
然后再看进阶版热图:
基因组共线性
WGCNA ggplot2版本
其他科研绘图
合作、联系和交流
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